until the Meiji Era, because Japanese orthography dates back to the Meiji Era.

modern Japanese literary text written in the Meiji Era often lacks compulsory voiced consonant marks.


Thus, we can get natural texts for evaluation of our method at low cost (in fact, it cost only a few weeks to annotate our above-mentioned test corpus).

we decided to begin with attaching voiced consonant mark for unmarked characters as a starting point for normalizing orthographic variations.


Since it is easier for human annotators to annotate unmarked characters than word boundaries and part-of-speech tags, we can obtain a large scale annotated corpus at low cost.

in this paper, we propose a classification-based approach to automatic labeling of voiced consonant marks as a preprocessing step for morphological analysis for modern Japanese literary language.


However, most of the historical materials that we deal with are raw text, and there exist little, if any, annotated texts with words and part-of-speech tags.

a word segmentation model learned from such a limited amount of data is unreliable.


However, modern Japanese texts contain many Kanji characters and relatively short Hiragana character strings because modern Japanese texts are similar to Kanbun-kundokubun, or the Japanese reading of a Chinese text.

hiragana ngrams fail to model error detection well for modern Japanese texts.


For example, if " " (deep) appeared only once in training corpus as in a word " " (is deep), then we will not be able to use the information " " in a phrase " " (if it is deep) when we classify a character " " in " ."

target character position ↓ -3 -2 -1 0 1 2 3 ⟨s⟩ ⟨/s⟩ ( Though we planned to publish a big magazine that compares favorably with the one in that country, )  we carry out clustering on Kanji characters and add character class n-gramin feature sets.


our method can not refer the discourse information since we only employed local context of character n-grams.

our method excessively tend to classify characters into unmarked.


Several studies have demonstrated that the use of unlabeled data can improve the performance of NLP tasks, such as text chunking (Ando and Zhang, 2005), POS tagging and named entity recognition (Suzuki and Isozaki, 2008), and parsing (Suzuki et al., 2009;Chen et al., 2009;Koo et al., 2008).

it is attractive to consider adopting semi-supervised learning in Chinese word segmentation and POS tagging tasks.


For a corpus-based word segmenter, a manually annotated corpus is essential.

we can easily compile a lexicon from a training corpus.


For instance, in addition to (研究(YanJiu), NN, HF) and (研究(YanJiu), VV, HF), (研究(YanJiu), VA, LF) and (研究(YanJiu), CD, LF) may appear as entries in the word unigram list, due to mis-tagging by the baseline POS-tagger.

we further impose a restriction based on the frequency as follows: if the number of entries with a HF label ≥ threshold, only the entries with HF will be selected, and if the sum of entries with a HF or M F label ≥ threshold, the entries with either HF or M F will be selected, otherwise, all of the entries in L m will be selected.


With tens and thousands of review texts being generated online, it becomes increasingly challenging for an individual to exhaustively collect and study the online reviews.

research on automatic sentiment analysis on review texts has emerged as a popular topic at the crossroads of information retrieval and computational linguistics.


However, the HL-SOT approach proposed in (Wei and Gulla, 2010) uses a globally unified index term space to encode target texts for different nodes which is deemed to limit the performance of the HL-SOT approach.

the LFS framework proposed in this paper aims at overcoming the weakness of the HL-SOT approach and consequently improving its performance by generating a locally customized index term space for each node.


For a target text the labeling processes of nodes on ∆ i locally can be considered as a multi-label classification process where each node is a local label.

the measurement for selecting terms as features should be calculated among nodes on the same hierarchy.


For example, "ergonomics" is a rare term but almost always appears in the class of "design and usability".

the MI algorithm can also achieve relatively better performance through favoring rare terms that always co-occur with specific classes.


As discussed in the previous paragraphs, we claim that incorporating information about a segment's neighbors, the classification of small text segments can be improved on.

we simultaneously determine the most probable classification of all segments in a review.


However, when the linguistic background is needed, it could be difficult for most people to reconstruct the whole sentence from the dependency trees in order to find the opinion passage.

if we want to find annotators to build a corpus which could be used to train an opinion relation recognizer, parsing trees are the better materials compared to dependency trees.


They designed formula for trios of each .

we adopted their rules on our augmentative experiment materials.


They did not predict on all dependency relations either.

there is no existing dataset containing correct opinion labels on dependency relations.


The performance in Table 4 is not promising.

we consider the structure of trios, that is, o left and o right should appear as an ordered pair, and otherwise the label was viewed as illegal.


For all we know, no previous work has annotated opinion information on all dependency relations, or mapped annotated opinionated structures to dependency relations on a large quantity of documents or sentences.

to the best of our knowledge, no statistically analysis of opinion dependency relations involving manually annotations has been conducted.


whether it appears to left or to the right of Krzysztof in the corpus.

strictly speaking our model does not generate the actual sequence of words in the corpus, but rather a collection of "documents" such as the above, or equivalently, a table listing bigram cooccurrence counts for each word type.


This similarity of usage is reflected in closeness of the distributions over the induced classes for those words.

the first class (colored red) is associated with many family names, the second (blue) with titles and first names, and the third (green) with companies and locations.


We convert the labeling to the BIO format which encodes chunking information into tokenlevel labels: each label is prefixed with B if it starts a new chunk, I if it continues the previous chunk, or O if it does not belong to a named entity chunk.

we end up with 81 labels after conversion.


However, the abundance of user-generated content comes at a price: there may be high-quality content, but also much spam content such as advertisements, selfpromotion, pointless babbles, or misleading information.

assessing the quality of information has become a challenging problem for many tasks such as information retrieval, review mining (Lu et al., 2010), and question answering (Agichtein et al., 2008).


However, according to the Pear Analytics (2009) report on 2000 sample tweets, 40.5% of the tweets are pointless babbles, 37.5% are conversational tweets, and only 3.6% are news (which are most valuable for users who seek news information).

when a user issues a query, recommending tweets of good quality has become extremely important to satisfy the user's information need: how can we retrieve trustworthy and informative posts to users?


we are going to investigate those factors that influence the quality of such short texts.


As mentioned, there are a large number of documents retrieved for each query while we only sample a small number of documents for manual annotation.

there are much more unlabeled documents yet to be utilized.


Thus we believe that a tweet with clear sentiment polarity will be more favorable for users.

we adopt a sentiment lexicon (SentiWordNet) and collect the top 200 frequent emoticons from our tweet corpus to identify positive and negative sentiment words.


As shown in the figure, we can see that the hypothesis holds.

we can safely formulate the hypothesis into a regularization factor in the subsequent experiments.


This happens because words that do not get translated using the statistical dictionary, are taken into account as they become part of concept mappings that have correspondence across languages.

these terms get accounted in the computation of the SM similarity measure.


This cluster mapped to a concept in Hindi which had words such as " Ê ", " " and " " where the first two words are translations for the words "communication" and "retail" respectively.

of this correspondence between the two concepts the words "bharti" and " " get associated.


In other words, for a given relation instance, we only expect two kinds of label sequences: (1) All y i are O, and (2) exactly one y i is B-REL followed by zero or more consecutive I-REL while all other y i are O.

the space of label sequences should be reduced to only those that satisfy the above constraint.


In addition, we have found that a large percentage of relation descriptors violate phrase boundaries: 22% in the NYT data set, and 29% in the Wikipedia data set.

it seems that phrase boundary information is not important for relation descriptor extraction.


For example, car models have replacement parts, are assembled by some maker in some year, and have an estimated current value.

known queries that refer to similar phrases may overlap significantly, or even become equal once the phrases have been replaced by a common slot filled by the phrases.


Consequently, known queries that refer to similar phrases may overlap significantly, or even become equal once the phrases have been replaced by a common slot filled by the phrases.

"kelley blue book value of 2008 dodge charger" and "kelley blue book value of 2008 honda civic" can be grouped into a shared query template "kelley blue book of 2008 ⋆", whose slot ⋆ is filled by the names of car models.


Second, known queries that can be grouped into a shared query template often provide a small sample of, rather than comprehensive coverage of, all phrases that would meaningfully fill the template.

new queries can be generated by filling the slots of query templates with new phrase fillers similar to known fillers.


In accordance with previously introduced methodology, an attribute is vital if it must be present in an ideal list of attributes of the instance (e.g., side effects for cloxacillin); okay if it provides useful but non-essential information; and wrong if it is incorrect (Paşca, 2007).

a correctness label is manually assigned to a total of 4,833 attributes extracted for the 75 target instances.


However, it is difficult to obtain them beforehand, because we have to check query logs to find incorrect queries and make necessary changes to define their corrections.

in this paper, we focus on query abbreviation and evaluate our proposed approach in an abbreviation expansion task.


Query expansion for a web-search query has to handle neologisms and slang on the web.

it is labor-intensive to maintain a list of correctly spelled words for search queries.


They suggested using relative frequency such as pointwise mutual information (PMI) and log-likelihood ratio as countermeasure against semantic drift.

we used pointwise mutual information (PMI) shown below to handle the aforementioned semantic drift problem.


Moreover, using PMI naively makes sparse matrix W dense.

we used normalized PMI (NPMI) (Bouma, 2009) below as the relative frequency and cut off the values lower than a threshold θ (θ ≥ 0).


They are computed as follows: In our experiment, the average number of candidates for each query is about 53.

we extracted 50 candidates from clickthrough logs and then reranked using three methods: 1.


As we mentioned before, this strategy is based on the findings that Web communities are characterized by directed bipartite subgraphs (Kumar et al., 1999).

our assumption is that the Web region comprised by this bipartite graph is rich in bilingual sites as backlink pages typically point to multiple bilingual sites.


The category class 4 (the usages of cardinal numbers and noun classifiers) was neglected, because we can treat cardinal numbers as adjectives or nouns and there are no true noun classifiers in English.

there are 28 constraints in total for English.


To our knowledge, we are among the first to report results for the full coreference task on this dataset.

our work contributes to the establishment of a baseline using a state-of-the-art supervised coreference model against which future work can be compared.


To do so, SVM light needs to position the hyperplane so that an instance with a higher rank in T is assigned a more positive value by the hyperplane than one with a lower rank in T .

we can apply the pairwise ranker to each test instance to be ranked, and use the value returned by the ranker for each instance to impose a ranking on the test instances.


An example of this is syntactic parsing in which long and complicated sentences will generate a large number of hypotheses and usually fail in disambiguating the attachments.

it is desirable to pre-process complicated sentences and generate simpler counter parts.


Since each simple sentence contains an object, we can group the candidate sentences by the object noun phrase.

it is not necessary to evaluate all combinations; we can look at one object at a time from left to right.


A simplification hypothesis s contains k simple sentences.

it is crucial that our model chooses reasonable simple sentences to form a hypothesis.


When the given documents are all supposed to be about the same topic, they are very likely to repeat some important information in different documents or in different places of the same document.

effectively recognizing the sentences with the same or very similar content is necessary for reducing redundancy and covering more diverse informative content in a summary.


The other type managed to obtain one or more regular clusters and a single noise cluster that contained all noises simultaneously during clus-tering (Li et al., 2007).

this type of approaches was able to provide noise-free clusters.


Given a sentence i that are in the same synset as t q are accumulated and contribute to be the weight of t q .

the cosine similarity between the two sentences can be defined in the query vector space.


Once a sentence is selected for a cluster, it cannot be assigned to the other clusters any more.

it can be naturally used to construct the cannot-link constraints with the other seeds.


On the   "sound"-"ASAP" weak association "urgently"-"ASAP" strong association Call Dialogues Call Logs other hand, an agent usually uses multiple expressions in a dialogue according to the situation or customer's level of understanding.

there is a gap between the expressions in the dialogue and in its corresponding log even though those are the same in meaning.


2 Consider Figure 1, for example; the LF method cannot regard as important the words not appearing in the log such as "unpleasant", "boom" and "sound".

the LF method may not include ID 7, which is important for contact center work, in the summary.


In the above situation, suitable utterances can be included in a summary by eliminating frequent utterances preferentially.

the AS with RFU method enables maintaining the quality of summarization with a high compression rate.


To examine the robustness to ASR errors, we calculated the reduction rate 3 of F-measure by comparing the performance to SR with that to MT.

the reduction rate of the AS method is 37.9% and the rate of the LF method is 43.1%.


Hidden states are labelled only by integers, hence there is no immediate indication of what sound a particular hidden state may represent.

a further decoding procedure may be needed to convert the true normalised form to a human-readable form.


The crux of the method is to compute the probability that an instance belongs to a class C i from a given closed set C, and hence assign the most probable class to a document D, consisting of a vector of n features x 1 ...x n : Bayes' theorem allows us to re-express this as: where P (D) is a normalizing constant independent of C i .

for classification, we only need to estimate P (D|C i ) and P (C i ).


Yang and Pedersen (1997) found that document frequency (DF ) and IG were strongly correlated.

we studied the relationship between LD and DF in our data, and found that low DF is a good predictor of low LD score, but not vice versa, as seen in Figure 2.


It makes no sense to attempt to classify documents in these languages since we will by definition misclassify them, so the results resported in Table 4 are only over languages in the evaluation set that are also present in the training set.

caution must be exercised in naively comparing accuracy figures across different combinations of training and test datasets.


In our approach, we use the cross-entity topic Wikipedia Categories to represent the semantic knowledge.

we model the contexts as the distributions over Wikipedia categories.


Wikipedia requires contributors to assign categories to each article, which are defined as "major topics that are likely to be useful to someone reading the article".

wikipedia can serve as a document collection with multiple topical labels, where we can learn the posterior distribution over words for each topical label (i.e.


We have mapped the contexts to a Kdimensional semantic space.

we can calculate the context similarity by their distance in this space.


However, from our observation some categories in Wikipedia may not be suitable to model the topics of a document.

we shall consider selecting an appropriate subset from the Wikipedia categories to effectively model the contexts.


However, some categories such as "people by status" and "Geography by place" in the Alladmin set cannot serve as the topics of a document properly.

we need to remove them from the category set.


To resolve the above issues, we change the batch size according to the variance of classifier's confidence on an un-annotated set.

we assign an integer to 1 and 2 in the first two iterations, and ( > 2) in the i th iteration is computed as below (Flexible Number): where is the confidence of the classifier on the un-annotated dataset at i th iteration.


For the computational cost, we note that document annotation is performed offline, while queries are typically short and thus query annotation and expansion could be done quickly.

the query answering time is not a problem.


Obtained values of the measures presented above might occur by chance.

a statistical significance test is required (Hull, 1993).


And in improved GVSM (Farahat and Kamel, 2010), the best which is covariance matrix of terms is calculated as 4where is random sample of and 5maps uncorrelated terms to nearorthogonal directions and negatively correlated terms to opposite directions in the semantic space, while traditional maps both uncorrelated terms and negatively correlated terms to nearorthogonal directions.

we use cosine similarities between term vectors in case of as one of our similarity measures.


In that case, the associate matrix becomes sparse and computational time can be saved.

in this work, we explore the following several word similarity measures in constructing word association and setup a similarity threshold.


However, we believe word co-occurrence in the test documents is also very useful.

we improve cross-lingual word similarity in the following way: statistical monolingual word similarity is computed from test documents first and a dictionary or translation probability as a bridge is used to get cross-lingual word similarity.


Nevertheless, there exists much noise in the microblog postings.

the maximum common substring might be a meaningless phrase or sentence.


However, seen from Figure 4, the right sub-tree is obviously not relevant to the dominating topic.

in this thread, the posting in the right sub-tree is deemed a spam posting.


Due to the quantity of spam postings is small, after calculating the similarity, the cluster which has the least postings may be filtered as the spam.

a clean and topic-related thread is obtained from thread .


Finally, a Dirichlet prior with parameter is placed on the topics .

the generative process is given as follows.


The citation text (a text snippet surrounding a citation) delivered by ParsCit is usually not sensitive to paragraph and sentence boundaries and may contain noise such as page number and footnotes.

we designed a text parser to get better citation sentences and related information by the following steps: 1.


Combined with the cue words defined in Table 2, especially with subject cue and typical verbs in class1 to class3, the contribution of these patterns to overall performance increases significantly.

our final feature set is the collection of these textual, physical and syntactic features.


That is, 30% of the instances are randomly extracted as training set and the remaining instances are set as test data.

according to our predefined class distribution, we set initial labeled data {20, 10, 8, 5}, and the rest of the training data are regarded as unlabeled data.


During the selftraining process, the training data changes slightly by adding few new labeled data each time, so the over-fitting problem occurs soon.

we choose  In this work, we defined a citation classification schema to better sketch the skeleton of a scientific paper from its background, fundamental ideas, technical basis and performance comparison.


While the first three refer to single aspects of a text, genres cover different properties of both documents and texts, such as their form, function, purpose, and target audience.

many different genre classification schemes exist, which makes most approaches to genre identification badly comparable as a recent study showed (Sharoff et al., 2010).


In this paper, we investigate why a text was written.

we introduce the ambitious task language function analysis, abbreviated as LFA, i.e., to classify the predominant function of a text as intended by its author.


PSO has been shown to be effective in solving a variety of complex optimization problems in practice.

pSO may also work relatively well in MERT since optimizing translation metric such as BLEU-score is also a complex optimization problem.


Next, the objective function F in PSO can be defined as a translation metric such as BLEU-score M shown in Equation 2.

in our case, F is calculated by using a tuning dataset D and system translations of the tuning dataset given by the current weight (position) x.


In PSO, each particle can individually update its velocity, position and personal best position.

we design MERT-PSO to work in a parallel computing environment.


This extension reduces the number of inefficient searches (and also the communication cost between particles).

this may also lead to a faster tuning process.


This is because Moses-MERT (parallel) runs the original MERT processes individually on every processors.

the runtime of Moses-MERT depends on the slowest MERT process for all MERT processes.


Computing gradients on the whole dataset before each update is too time consuming.

we computed the gradients on mini-batch of 100 sentence pairs instead.


It is known that combining the outputs of multiple system combination procedures can produce further gains.

our goal was to design a complementary combination system which could be used in conjunction with our current system to produce better results than either could independently.


The advantage of this is that it quickly converges even when tens of thousands of weights are estimated simultaneously.

instead of discretely selecting a 1-best hypothesis from each of the m test sentences, the BLEU statistics are summed over every hypothesis weighted by its corresponding posterior probability.


(2) pens ACC pick up "(I) start writing" The data used in the Japanese WSD task is part of the BCCWJ corpus.

we use the remainder of the BCCWJ to extract the training data.


The underlined part is exactly the same as the example sentence in (2).

we tag toru "pick up/take" with 37713-0-0-1-2 and add this entire sentence to the labeled training data.


Analytical Setting One anticipated problem with our expanding method in Step-1 is that the extracted data may have a different sense distribution from test data.

to investigate trends based on sense distribution, we employed the entropy E(w) of the frequency distribution in given training data, which is given by where p(s k |w) is the probability that word w will be sense s k .


However we could use only 362 example sentences to extract labeled instances, such as in a sentence (3).

1,088 (=1,450-362) example sentences did not used to extract labeled data.


However, it may be difficult to achieve any further improvement because the extracted data may have an unnatural sense distribution and limited variations.

to realize an improvement, we employ a semi-supervised learning method in Step-2.


As described in § 5.1, it has been reported that the MHLE was robust even when the labeled and test data were very different, as with the Japanese WSD task, which came from heterogeneous corpus.

we can say that this semi-supervised WSD using a hybrid generative/discriminative approach (MHLE) is effective (with respect to purpose (a) above).


One of the advantages of our method is that it can provide training data even for unseen senses (which did not appear in the given training data but were in the dictionary).

we investigated the accuracy for unseen senses.


As the semi-supervised learning method, we employed the hybrid generative/discriminative approach (MHLE), because this method has been reported to be robust even when the labeled and test data were very different as in the Japanese WSD task, which came from heterogeneous corpus.

in this step MHLE achieved a good improvement (82.8 %), even when using only given training data as labeled data.


However, WordNet-based WSD methods usually achieved lower performance compared to supervised methods, mainly due the fact that the lexical and semantic knowledge contained in WordNet is not sufficient for WSD.

many methods (see Section2) have emerged to enrich WordNet with the lexical and semantic knowledge for WSD purpose, such as by using Wikipedia, on-line lexicons, domain, and so on.


In addition, ConceptNet defines nearly thirty kinds of semantic relations, such as CapableOf (agent's ability), SubeventOf (event hierarchy), MotivationOf (affect), Desir-eOf (want to), and so on, most of which are not included in WordNet.

if extending WordNet with the large amounts of semantic relations contained in ConceptNet, it is desirable to improve the performances of WordNet-based WSD methods.


However, all above methods mainly aim to enrich lexical and taxonomic resources.

some recent work (Mihalcea, 2007;Ponzetto&Navigli, 2010) exploits Wikipedia, a large collaborative Web encyclopedia, to extract the knowledge for WSD.


As a result, we calculate the score of the WSP for each sense, and choose the sense with the lowest WSP score as appreciated one for the ambiguous concept in the assertion.

for each ambiguous concept of every ConceptNet assertion, we can assign the appropriate sense to it according to the WSP scores; and the resulted ConceptNet can be used to extend WordNet.


That is, we only need to consider the relatedness between d and the term in WSP(c).

we sometimes simply say the NGD score of a term in this paper without confusion.


However, it is inevitable that there are some noisy terms in WSPs, which will dramatically decrease the performance of disambiguating ConceptNet.

we need to pay efforts to reduce such noises.


There are two kinds of noises: those from WSP of the correct sense, and those from WSP of the wrong sense(s).

suppose we have an assertion <c, relation, d>, where concept c is ambiguous and has two senses Then the first kind of noises are from WsP( 1 n c ), which do not have close relation to d. their NGD scores should be high, though they have a WordNet relation to 1 n c .


Firstly, Threshold filter cannot effectively remove the second kind of noise terms, because it may filter out too much right terms from the WSP of a wrong sense, while remain terms be-longing to the second kind of noise.

there are not enough right terms to relieve the second kind of noise terms for the wrong sense.


Although hypernymy, hyponymy, and meronymy/holonymy are transitive, and can generate the indirect WordNet resources, the number of meronymy/holonymy is far below than those of the other two in the WordNet.

we ignore the indirect WordNet resources derived from meronymy/holonymy.


Especially, hypernymy→hyponymy dramatically decreases accuracy (-8.94% compared to the "six resources").

we do not list the accuracies of combining other indirect resources with the direct ones).


Similarly, if we are given a corpus of another language (say, Hindi) belonging to the same domain (i.e., Sports) then we would expect to see more words which are manifestations of the second sense than the first sense.

we can estimate the probabilities of different senses of the word 'facility' by looking at the counts of its translations in different senses.


Note that every word in the Marathi synset is considered to be a translation of the corresponding words in the Hindi synset.

the Marathi words mulgaa, porgaa and por are translations of the Hindi word ladakaa and so on.


However, Static Multi Prototypes with Guided Sense Selection still fail to perform better than Static Single Prototypes.

we can conclude that the lower performance of Static Multi Prototypes cannot be attributed to the sense selection process only.


For the ACE 2005 event extraction task, Attack events have the highest frequency in the training corpus (2240 times, an average of 4 events per document), while Start-Position events only appear 232 times (an average of 1/3 event per document).

to acquire enough training samples, we need to annotate a lot of documents.


However, although the trigger itself is crucial to determine whether or not there is a reportable event, it is not always sufficient.

most current event extraction systems consider trigger and argument information together to tag a reportable event.


If a sentence S in the sample pool shares many words with another sentence in the training data known to contain an event, and the event tagger does not find a trigger word in S, there is a good chance that S contains a new (previously unseen) trigger word and new local structure, because the two sentences may be describing the same event, but using different verbs and word sequences.

we apply a pseudo co-testing algorithm with one view from an event tagger based on local information, and another view, which aimed to solve an approximate task: whether there is a possible event in a sentence.


In general, a sentence will be represented as a vector of features € S 1 = { f 11 , f 12 , f 13 ,....., f 1n } and the similarity is calculated based on the feature vectors of the two sentences.

the essential problem becomes how to build the feature vector for a sentence.


These segments are frequently discontinuous as the structure of news articles is often dominated by moves between the main event and past similar events; • filling event templates from relations: since event segments go beyond the sentence level, they are even more likely to contain complex relations than sentences.

we have to verify which entities mentioned in these segments are eligible to be part of the complex relation.


As the PageRank strategy only relies on the graph structure without considering the weight of the edges, its highest ranked entities are those that are highly connected regardless of the weight of the edges.

if several non fillers entities are strongly linked, they get better scores than the others.


an order of magnitude of 10 14 iterations 2 .

we use a more efficient procedure formally described in Alg.


Note that in (Nguyen et al., 2009;Zhou et al., 2007;Zhang et al., 2006), even for tree kernels, the tree structures were also integrated with entity types (see Figure  1 as an example).

in the parse trees in Figure 1, we replaced entity types PER, ORG, LOC with a generic type ETYPE.


Extracted sentences were postprocessed to fit the natural sentence form; that is, with first characters capitalized and question marks or periods added as appropriate.

we extracted 750 imperative sentences and 1,241 question sentences from 24,243 sentences.


QuestionBank does not provide function tags, and therefore in training and evaluation of the parsers, abstracted dependencies were extracted from the corpus.

a parser adapted to one domain could not provide correct dependency labels on functions for the other domain.


That is, we can obtain convergence to different local optima.

we test several initial values and adopt the resulting parameter that maximizes posterior probability.


However, since the ni (dative) case of 'yaku (1)' rarely appears, we assume there is no case slot if there is no overt argument.

we use the log occupancy of the case slot, whose value is the same as the log of the generative probability of a case slot in Kawahara and Kurohashi (2006)'s model and estimated from case structure analysis of a large raw corpus: e.g.


If the weights for these features become larger, corresponding zero pronouns are assigned to antecedents more often.

the weights for these features are regarded as parameters to control the recall/precision trade-off.


Although we mainly evaluate our system by using the F-measure, the algorithm mentioned in Section 4.3 does not select a parameter that maximizes the F-measure.

after parameters are estimated by the algorithm, we adjust the weights for these features to maximize the F-measure by using training or development data.


However, when all generalized example PMIs were eliminated, performance worsened.

we considered that cluster PMI, category PMI, and NE PMI could be clues for zero anaphora resolution, and confirmed that zero anaphora resolution could benefit from overlap-  We think this is because case/location features involve salience information.


original form annotated even for predicates that appear in passive or causative voice, our model outputs the surface predicate-argument structure.


All word forms are derived by applying various morphological rules defined in the GG to the word stem.

we need to add only the correct stem of the unknown word the lexicon.


The main issue with adverbs is that many of them can be used as adjectives as well.

the classifier has a strong bias towards predicting an adverb type for words for which an adjective type has also been predicted.


Despite the higher number for realisations per sentence for the test set, the quality of the realisations is the same for both sets-for 60 sentences a correct realisation is produced.

the entries acquired with LA can be employed for both parsing and realisation.


Also, rules with the same left-hand-side have been found to be consecutive in the collection.

the outcome of previous rule's check is reused if the current rule is same as the previous one.


Japanese function words are often unaligned, similar to this example, but the dependency relations between subtrees stepping over the function words are assumed to hold on the other side.

we introduce a pseudo parent to capture the relations.


For example, in Figure 1, traveling from "A photogate" to "photodetector" requires 1 step going up (to reach "is used") and 2 steps going down (via "for"), so the dependency relation is (U p, Down) = (1, 2).

the tuple is represented as a triplet of non-negative integers R f = (N, U p, Down).


Instead, our goal is to grub knowledge from paraphrases and enrich the translation corpora.

within the application-driven paraphrase generation framework, we propose a specific paraphrasing usability model, sentence novelty, for selecting paraphrases which contain the most novel n-grams to the translation model.


Given a paraphrased sentence T, which consists of J words, the novelty function Novel(TM,T,n,j) judges whether the occurrence of t j generates a new n-gram to the translation model (TM) according to the prior n-1 words of t j .

formally, the novel function for position j can be defined as: the novelty model for a paraphrased sentence T, considering the novelty of 1-gram to Ngram (N=4 in this work), is computed as: where ߣ is the weight for the novelty model, Now we can describe the complete formula of the SPG framework as: We enhance the SMT model by expanding the training corpus using paraphrases.


To grub knowledge from paraphrases as much as possible, we exploit two different strategies for paraphrase generation in the experiments: (1) generating 1-best paraphrase for every source sentence in the training corpus, and (2) generating k-best paraphrases for a source sentence and selecting m sentences from them which have the most novel n-grams.

we get two paraphrased bilingual corpora besides the original corpus.


The reason of not using all the k-best results for improving SMT is that the top-k paraphrases generated for a sentence are generally very similar, if we train the SMT model on all these sentences, it would be quite time-consuming and much of the computation is vain.

we propose an algorithm to select a subset from all the paraphrase sentences, which can cover most of the newly introduced information while dramatically reduce the numbers of paraphrases.


Considering the different reliabilities of these corpora, simply merging them into a new corpus and train a translation model is not the optimal solution.

within a phrase-based SMT framework, we train three phrase tables from these corpora, and then integrate these phrase tables with different weights.


Semi-Markovbased algorithms, such as semi-Markov perceptron and semi-Markov CRFs, enumerate NE candidates represented by word chunks in advance for capturing features such as the first word of a chunk and the last word of a chunk.

the computational cost of a semi-Markov perceptron is O(KLN ), where L is the upper bound length of the entities.


We compare C 1 and T i,1 again, and C 1 is equivalent to T i,1 .

we generate a training sample for applying REDUCE=O to C 1 by (S1), and move to the next word chunk.


C 2 is still not equivalent to T i,2 , and the NE label of the last word "went" is "O".

we generate a training sample for applying POP to C 2 by (S2), and apply POP to C 2 is equivalent to T i,2 , and a training sample for REDUCE=P ER is generated.


This indicates that Semi-Markov handles about L times larger space than the other algorithms in terms of the number of nodes.

it requires more memory usage than the others.


In Chinese, a character is usually a morpheme.

the CWS model based on the character tagging framework can be regarded as a UW extraction method using morphological information.


In contrast to the first metric, the PRF scores are calculated based on the sums of TP, TN, FP and FN for all instances in the test dataset; we further consider whether the boundary matches that of the linked KB entry's mention.

under this criterion, an FP could link a true entity to the wrong KB entry or link a false entity to any KB entry; while an FN could link a true entity to the wrong KB entry or fail to recognize a true entity.


A clustering approach would first put two of the words into a single cluster; the remaining word would be added due to its similarity with one of the first two words.

even though the cognate relationship between beva and vivir may not be found directly, the links beva-bivi and bivi-vivir are enough; our clustering method finds a cognate pair that would otherwise have been overlooked.


For this reason, it was not possible to select a synset for 174 trgs out of the 1,800 samples in EB1 and EB2.

we used 1,636 samples in evaluating EB1 and EB2.


The Japanese WordNet does contain other meanings for the term anime, such as a hard copal derived from an African tree (synset 14896018-n) and any of various resins or oleoresins (synset 14766265-n).

japanese animation films with the hypernym anime are linked to either 14896018n or 14766265-n.


To evaluate our WordNet-Wiktionary alignment, we follow the methodology of previous approaches and compare the result of our automatic alignment algorithm with human judgments.

we create a new manually annotated dataset, as we are not aware of any other datasets that could be used for this task.


However, the words "bluebacked fish" and "cerebral thrombosis" do not cooccur in any four sentence window in the 600M Web page corpus used in our experiments, which was crawled in 2008.

"PREVENTION(bluebacked fish, cerebral thrombosis)" is an NS instance and exemplifies the importance and necessity of inferring NS instances.


Answering this question precisely is difficult because of anaphora and ellipsis.

we assume that if the two nouns of an NS instance do not co-occur in any four sentence window ("N4S") in the corpus, the instance is unlikely to be mentioned explicitly in any form in the corpus.


We think considering constraints among relations might contribute to improve the performance of identifying relations.

we realize it with a Markov logic network.


A thread consists of a question and some answers and the answers are sorted in order of posted time.

in this paper, we try to identify to which preceding answer and in what relation an answer is related.


However, some answers are irrelevant to a question and these answers might be unnecessary for an overview of a thread.

we con-sider not only answer-answer relations, but also question-answer relations.


In first order logic, if there is at least one predicate that violates formulas in a possible world 4 , the world is not valid.

a model with first order logic can only have strict constraints.


It is obvious that a possible world where soft constraints are satisfied is more probable than a world where soft constraints are not satisfied.

the model identifies relations to satisfy as many of these constraints as possible.


Also, we used a different data from their experiment.

we could not conduct an accurate comparison experiment between our model and their model.


As mentioned above, the cQA archives are frequently searched and viewed through search engines.

we can find a large number of records in search engine query logs, in which users searched a query Qr and clicked on a question Qs from the cQA archives.


The manual methods (Hermjakob, 2001) use hand-written rules and heuristics to do the classification, thus it is time consuming and hard to extend to new question categories; while the automatic methods classify the questions based on machine learning technologies or statistical methods, thus are much more efficient and easy to extend to new question types.

most of the work follows the automatic methods, which will be also adopted in our work.


However, the class related words may be nouns, verbs and adjectives, and we have found that the verbs and adjectives would cause the ambiguity in our test.

we only use the nouns in the class related word sets collected by Li et al.


All in all, our proposed ExCSR including all of the three distance constraints is an effective model to the question classification.

in the comparison experiment, we consider all of the distance constraints.


Among the 12, 880, 882 questions we have collected, only 1.87% occur more than once.

a simple ranking scheme based on frequency is not feasible.


We then suggest refinement words that maximize entropy gain.

a satisfactory question can be generated in fewer iterations.


The topic descriptions usually consist of several complex questions such as "Describe theories on the causes and effects of global warming and arguments against them."

many approaches such as LexRank (Erkan and Radev, 2004) focus on compressing the relevant documents.


Furthermore, damage to communication equipment, power outages, and inundation of the air waves prevented the use of mobile phones, which are generally the most important tool for communication during a disaster.

twitter or social network services (SNS) such as mixi 3 played an important role for propagating safety information among people.


Tweets containing RT (re-tweets) and QT (quote tweets) were removed to eliminate duplication.

61,376 tweets were collected from March 13th 1:37am until March 14th 16:45pm.


This is due to the fact that all other components of the system depend on MA, and cannot be developed until MA is in place.

we utilized existing general domain resources, and added new resources as they were collected in a domain-adaptation framework.


This is because of the fact that almost all documents are written in a single language hence similar meaning words from different languages will never share similar context.

even though a topic model applied on a bilingual corpus will find coherent topics (for each language independently) it will bifurcate the topic space between the two languages.


And, most of the current Q&A forums haven't had a efficient mechanism to identify threads with nearduplicate content.

users have to go through different versions of duplicate or near-duplicate content, and are often frustrated by it.


The number of threads grows in a significant pace compare to other media.

a more efficient method need to be used to handle millions of content.


Our work is motivated by related entry suggestion in online encyclopedia construction and query suggestion in Web search.

we are interested in finding related persons given a query person q.


We first normalize the input vectors by the sum of their components, and calculate the JSD as described in (Lee, 1999): where KL denotes the Kullback-Leibler divergence between two vectors, and Note that the larger the JSD is, the less similar two vectors are.

the similarity between vectors Web search queries represent the demand of information from the users.


In LDA, the expectation-maximization algorithm cannot be used to estimate the parameters, since the computation of the posterior distribution of latent variables is intractable.

a wide variety of techniques using the variational method and Gibbs sampling, have been proposed to estimate the parameters (Blei et al., 2003;Griffiths and Steyvers, 2004).


Furthermore, the task of sentence ordering in multi-document summarization is harder than that in single document summarization because multiple documents are created by different authors who have different writing styles and backgrounds.

no natural order of texts can be extracted as the basis of sentence ordering judgment.


Each summarization sentence is extracted from a source document even though it is created or rewritten manually, it also can link to one or more sentences in source documents.

each summarization sentence is also not isolated and there are many sentences surrounding them in the source document.


In the DUC04, sentences in summaries are not directly come from the source documents and they are created by manually.

we need map them back to the sentences in source documents.


But it's difficult to give the order for the sentence base on a corpus without the source documents even for a knowledgeable man.

we believe the source documents can give us some effective and efficient information.


Comparing figures 3 and 4 reveals a more straight line when using a large RI-space.

by using an outside RI-space, the effect of randomness is reduced and a more predictable result between seeds is achieved.


Several successful attempts have already been made in the direction of automatic classification of connectives, while token-level argument segmentation has not been explored.

in this paper we will focus on the segmentation and labeling of discourse arguments (Arg1 and Arg2) with full spans, as defined in the annotation protocol of the Penn Discourse Treebank (PDTB) (Prasad et al., 2008).


In this optimization scheme, the bestperforming set of features is selected on the basis of the best F1 "exact" scores.

we increase the number of features at each step, and report the corresponding performance.


We observe that the performance of the lemma increases if integrated with the inflection feature, while inflection in isolation scores a null Precision, Recall and F1.

we consider lemma and inflection together as a single feature, which we call Morph.


This study was motivated through a limitation we observed in conventional lasso: that features could be inter-related, but such dependences are not incorporated within the current regularization.

the conventional method tends to favor correlated features, which can lead to the importance of non-correlated features being neglected.


Each of these decisions is made upon the score of local base-classifiers only, and not changeable after that.

a wrong decision inevitably leads to a group of wrong predictions.


First, TO was assigned when the "ratio of two simulation accuracies >= 0.40" was false in the root node of the decision tree.

tO was assigned to cases whose ratio of "Simulation accuracy of theOther / Simulation accuracy of target Only" was lower than 0.40.


Some previous studies (e.g., (Carlson et al., 2009)) showed that constraints are useful for self-learning or semisupervised learning in terms of quality control.

in addition to the ranking scores above, during the selection process we consider more constraints.


Although adding dependency features obtained small improvement for the cross validation result, it degraded the performance on test set (see last row in Table 2).

we exclude that feature for subsequent experiments.


The results shows that simple re-sampling only obtained the accuracy of 74.58%, which is even worse than using the entire data set.

we can conclude that the improvement we observe is mainly from the iterative learning method.


However, our Markov Logic approach creates a joint model for the three cases and finds the most probable assignments taking into consideration the dependency between them.

our model can prevent such an unlikely result (See the right box in Figure 2).


The joint approach using Markov Logic is computationally hard even if it targets only intra-sentential PA relations.

extraction of inter-sentential PA relations which are crossing sentence boundaries is intractable.


It is often difficult to extract Zero-Intra PA relations with only local features because syntactic dependencies between them are weak.

our global constraints contribute to find correct assignments of PA relations and we got a large improvement in Zero-Intra.


Moreover, in global model, � delete(1), delete(2), delete 7� are also output and "��" and "��" did not become argument candidates.

"��������" was correctly selected as a ga-case of "��".


We assume that coherence refers to the establishment of a logical sense among different sentences of a text.

it is a principle of interpretability related to the communicational situation and to the capability of the reader in calculating the meaning of the text.


We manually revised the resulting annotated corpus and corrected possible mistakes made by AZPort.

the noise from the automatic annotation of rhetorical structure does not interfere in the coherence annotation.


In our observations, the relatedness of these categories of sentences to the title depends on other aspects than coherence, like the very nature of the research being reported.

we cannot assume that the lack of a strong relationship between a sentence of these categories and the title may indicate a coherence problem.


The relationship between a rhetorical component and other components dictates the global coherence of the text (Higgins et al., 2004).

for an abstract to be easy to follow and understand, the rhetorical components must be related.


Thus, it is expected that the Gap component is related with at least one sentence of Background.

we understand that the absence of relationship between these components can be an indication of a coherence problem.


On the other hand, taking into consideration the annotation process, we observed difficulties to label the sentences with regard to the Dimension Linearitybreak.

due to the annotation ambiguity and the low number of examples found, we do not present the classification model for Linearitybreak in this work.


Intuitively, sentiment or subjectivity are context dependent.

modelling topic coupled with sentiment should serve a critical function in sentiment analysis.


The LDA model (Blei et al., 2003), as shown in Figure 1(a), has been used as baseline in document-level sentiment classification in previous research (Lin et al., 2010).

we also evaluated LDA on the sentence-level subjectivity detection task by modelling a mixture of three sentiment topics, i.e., positive, negative and neutral.


For fair comparison, we encoded prior knowledge of sentiment lexicon into LDA as identical to sub-jLDA.

the LDA model here can be considered as a weakly-supervised version.


we conclude that subjLDA is superior than LDA in the subjectivity detection task.


Examining Figure 2 reveals that, while the 4,500 least frequent neutral words appear 11,142 times in the corpus, the 1,957 most frequent words (i.e., from 4500 to 6457) appear 93,036 times, nearly 10 times as much as the former.

the high frequency neutral words become dominant in the model and result in severe classification bias towards the objective class.


Thus, the high frequency neutral words become dominant in the model and result in severe classification bias towards the objective class.

appropriate filtering of neutral words is necessary in order to avoid introducing bias into model learning.


e.g., "picture quality" in "the picture quality of this camera is great," where "great" solely determines the sentiment of the sentence.

resource terms require special treatments in sentiment analysis.


In English grammar, quantifiers usually precede and modify noun terms.

after locating a quantifier in a sentence, we extract its associated noun term, which directly follows the quantifier.


Moving to the clause level is also not appropriate, since several opinion expressions can be discursively related as in The movie is great but too long where we have a Contrast relation or as in Mr. Dupont, a rich business man, has been savagely killed where we have an Elaboration because the appositive gives further information about the eventuality introduced in the main clause.

we need to move to a finergrained analysis, at the segment level.


For each entry E L in our lexicon, we manually look for its corresponding senses in D as follows: if E L ∈ D then if SubjSense E L ⊆ Sense E L then add to our lexicon the set SubjSense E L .

for noble we only retain (b) and (c) as subjective senses.


In order to guarantee the convergence of the procedure, we rely on the idea that the goal of the Op classification is mainly to detect OO EDUs among intrinsically O EDUs.

from the perspective of the Op partitioning of the EDU space, the classification is supposed to start with all the input EDUs as Non EvalOp, and then to move the appropriate ones into the Eval Op class.


As a result, it has proven difficult for them to use further agglomerative processing to induce simple distinct syntactic categories which map to POS tags naturally (Christodoulopoulos et al., 2010), and thus operating in a mode which achieves a higher tagging accuracy with coarser categories is not available to those systems at all.

achieving high accuracy with a smaller tag set is the harder, not easier, task for those systems.


As illustrated at step 3 of Algorithm 2, the core idea is to use English segmentation learned from CE alignment (step 2 of Algorithm 1) and already-aligned intersection part of EJ (iEJ, step 3 of Algorithm 1) to constrain the EM alignment process.

the English bigram LM and the aligned intersection part (iEJ) keep unchanged during all the EM iterations.


All of the previous work on machine translation works on phrasebased statistical machine translation.

their translation model is to calculate phrasebased conditional probabilities at unigram level ( ) while our transliteration model is to calculate joint transliteration unit-based conditional probabilities at bigram level ( ).


This makes the transliteration from English to C/J a many-to-few mapping issue and backward transliteration a few-to-many mapping issue.

backward transliteration has more ambiguities and thus is more difficult.


 The nature of transliteration is a phonetic translation process.

a little bit variation in orthography may not hurt or even help to improve transliteration performance in some cases as long as the orthographical variations keep the phonetic equivalent information.


When comparing our error patterns with those of their model, although the overall increase and decrease of the error pat-3 although VV → NN errors look like an exceptional case terns look largely similar, our model has a relatively smaller number of increased error patterns than the decreased ones.

by selectively improving syntactically-important tags, our joint model is considered to have improved the POS tagging accuracy over the baseline tagger.


which are derived adverbs of the same formal type have a regular formation, in the sense that their interpretation is homogeneous.

nuitamment "by night" is an isolated case, as opposed to an open series of identical forms with a different interpretation.


It is also impossible that annotation guidelines would identify a unified phenomenon and propose two analyses based on arbitrary conditions (say, the first letter of the head noun).

annotators must learn how to represent each phenomenon, and then must decide which phenomena a specific data token exhibits.


But it gets case from the matrix predicate and hence also has a relationship with the matrix predicate.

seema-ko corresponds to two positions in the Ps tree: a lower position (the empty category *CAsE*) as the subject of the lower predicate and a higher position as the object of the higher predicate.


In LPCFG, non-terminals are annotated with lexical heads and the probabilities of CFG rules are estimated conditioned upon these lexical heads.

lPCFG becomes sensitive to lexical heads, and its performance is improved.


As mentioned, first-order lexical dependencies are quite limited, and thus may lose much information about the grammatical relations between words.

the performance improvement of this approach is limited as well.


Forest reranking, by contrast, can rerank a packed forest of exponentially many parses, and thus provides a good way to overcome these limitations.

we also use the forest reranking method, based on higherorder lexical dependencies.


Concerning the PCFG model, for preliminary studies we used our own implementation, but for this work we used the much faster implementation described in (Johnson, 1998).

6 Concerning data, for preliminary studies carried out to validate our incremental training procedure, we used the same data used in the ESTER2 named entity detection evaluation campaign, our results can be directly compared with those reported in (Galliano et al., 2009).


More precisely, suppose we want to translate a sentence from one language to another, and some methods have been applied to generate all the target words, whereas the words need reordering because two languages may have totally different grammars (like English and Chinese).

our goal is to recover the correct, or reasonable, word order of the target sentence.


There are about 0.307 million English sentences in the material.

the trigram language model built by SRILM (Stolcke, 2002) can reflect the properties of the English language.


All the existing methods and standards are designed to test the accuracy of the translation results, but not the single process of word-reordering.

we designed an experiment model and a testing principle for our own purpose, shown as follows.


The experimental result shows that setting (k, l) = (4, 3) gives the best outcome among all chosen parameters.


In addition, the sub-phrase pairs have their own probabilities and we should incorporate them to better estimate the probabilities of the hierarchical rules.

the calculations of relative frequencies and lexical weights for hierarchical rules are more complicated.


While frequency indicates how often the source phrase and target phrase occur together, lexical weight models the correspondence between them.

we can construct a scoring measure that incorporates both frequency and lexical weight.


We denote R as the variable rule, P as the phrase pair (f j 2 j 1 , e i 2 i 1 ), and X k as the kth sub-phrase pair that is replaced with X.

the inside probability of a variable rule is calculated as: We tried to follow the constraints of Chiang (2007): (1) unaligned words are not allowed at the edges of phrases; (2) a rule must have at least one pair of aligned words.


There are 25,921 RMWEs in the training corpus.

an additional phrase table containing the automatically extracted RMWEs is constructed.


In this paper we have introduced new objective functions and argued that they alleviate some of the deficiencies of standard maximum likelihood for conditional models.

we are interested in comparing models and objective functions, and not so much in getting the last bit of practical performance (f-measure).


Given the observation that more recent training data seems to be more important than older one, we apply an exponential decay function: where λ is the decay factor and ∆t is the discretized time distance (0 for most recent part, 1 for the next one, etc.).

our weighting scheme has only one parameter to be optimized.


The difference in the quantity of data taken from most recent and least recent data is only 1.4% (which still represent 200k sentences).

one could think that the weighting does not favor recent data that much.


We create a homogenous name list for those proper names used in the test query topics and uniformly place those names into the original document collections.

each proper name in the query is replaced by 4-5 different names with similar pronunciation.


In fact, the accuracy of the NLU-SA labels in this data set, with respect to the gold SAs, is 53%.

while Rules can achieve very good performance with gold SAs, the high frequency of NLU errors causes a significant degradation in policy performance.


9 minimizes L() on D. 1: Compute true loss L(e|e r ) ∀e ∈ N (f ) for All pair e i , e j ∈ N (f ) do Add (e i , e j ) to P if L(e i |e r ) < L(e j |e r ) 6: end for 7: end for 8: .

in light of the fact that posterior probabilities p(e|f ) are not reliable, we directly compute the true loss (using a development set) and ensure that our GMBR decision rule minimizes this loss.


We also implemented it but the results were similar that those using MBR decoding in each system and keeping the 1best translation.

we maintained MBR decoding for the rest of the experiments, which is also easier to work with.


To our best knowledge, there is no such evaluation on Chinese sentences.

this paper implements two baseline systems: pointwise mutual information (PMI) and a 5-gram language model that are widely used in previous work for Chinese nearsynonym choice evaluation.


In the field of lexical semantics, the contextual information is useful for representing the meaning of words, phrases, as well as sentences (Mitchell and Lapata, 2008;Erk and Pado, 2008;Thater et al., 2010;Ó Séaghdha and Korhonen, 2011;Grefenstette et al., 2011).

the co-occurrences between a target word (the gap) and its context words have been commonly used in statistical approaches to measuring the substitutability of words.


Similarly, ( ) is the number of times x occurs in the corpus, and ( ) ( ) P y C y N = , where C(y) is the number of times y occurs in the corpus.

(1) can be re-written as  The near-synonym with the highest score is considered as the answer.


However, Wikipedia is the free online encyclopedia, and everyone can access and edit the information.

it is often included noise information such as categories which do not characterize the cluster in the pages.


In this case, it must signal the decoder by transmitting an escape event.

space must be reserved for this event in every probability distribution that encoder and decoder maintain.


Relationx,y = 1 − max(log|A|, log|B|) − log|A ∩ B| T − min(log|A|, log|B|) Here A and B are the set of articles which link to the articles x and y respectively, T is the total number of Wikipedia articles, A ∩ B is their overlap.

for every W ikipedia word, we find the semantic relatedness to all other such words.


For instance, SLA research using learner corpora examines learners' proficiency on the basis of what vocabularies/grammars learners actually use (Tono 2009, among others).

most learner corpora are compiled of linguistic outputs that learners produce in speaking and/or writing.


there is a shortage of language output data of listening, and furthermore we have to construct a learner corpus that integrates all these eight types of data.


Though there are other contexts such as academic/professional contexts, these contexts contain more nonlinguistic aspects.

we chose daily-life contexts in order to minimize non-linguistic aspects such as background knowledge.


Unclear questions may decrease chances of getting good answers.

it is important to investigate supporting methods of making clear questions.


Then, there were 106 of these 107 cases where questioners obtained good solutions or useful clues.

questioners can increase their chances to obtain good solutions or useful clues when they accepted indications from answerers and modified their questions.


chiebukuro, 474687 answers were submitted and 171848 of them (36%) were received good evaluations.

many questioners gave good evaluations to answerers who indicated unclear points of their questions.


The reason is that since the predicted types of a question are already diversified by CogQTaxo, incorporating it into question re-ranking already enables us to diversify the infoNeeds in the results implicitly.

the explicit diversification step enhances the performance further.


First, the definition of discourse is unclear and varied over different areas.

finding the clear boundary of a discourse argument is a vexing problem by itself.


Second, the relationship between arguments is often difficult to decide and inherently subjective.

the annotation and the evaluation are problematic and laborintensive.


Firstly, POS tags are indispensable features for dependency parsing since pure lexical features lead to severe sparseness problem.

pOS tagging is traditionally considered as a supporting task for dependency parsing.


As the confidence values are low, major arcs in these sentences might be wrong.

these sentences were giving negative impact on the parser performance.


For any contrastive studies between Cantonese and Mandarin, a corpus balanced between formal and colloquial registers would be desirable.

the TV drama provides the colloquial register; the news program contributes mostly to the formal register with the speeches of the anchor and reporters, but also some colloquial register with those of the spontaneous interviewees.


Although it is important to use powerful machine learning algorithms, latest studies have found that large-scale and high-quality corpora are more important for WSD (Agirre and Edmonds, 2006).

building such corpora is key challenge to be addressed.


However, the cost of annotating a necessary size of corpora is prohibitive.

in the research field of WSD, some common ambiguous words are sampled and then annotated with a necessary amount of examples.


Because the user does not know which word the system knows, he has to input the contents of both words.

once the confirmation function is successfully achieved, the content of the unknown word can be obtained.


They must choose each word sense carefully in order to pass confirmation stage.

they provide the correct sense for the ambiguous word of unknown question.


Because of the incompleteness of the thesaurus, there are some words whose senses cannot be found.

we add a "其它 (other)" option for every word since the current word sense does not belong to any of above options.


Table 5 shows that both proposed methods perform substantially better than CONG: ANS CT exceeds it by 43.6% and ANS-ACK PCT surpasses it by 75.1% (F score).

we can conclude that with very small training data, it is possible to achieve high accuracies compared to extracting them in an unsupervised manner.


As indicated in (Wan and Xiao, 2008b), performance on test set does not change much when cooccurrence window size W ranges from 5 to 20, and (Liu et al., 2010) also reports that it does not change much when topic number ranges from 50 to 1,500.

we pick co-occurrence window size W =10 and topic number |T |=60 to run WordTopic-MultiRank model.


However, for INTERS, no further properties are specified in order to identify it -the information of being healthy is not telling as we actually want to find out how to detect healthy food.

instances of type INTERS are hardly informative when it comes to answering whether a particular food item is healthy or not.


In addition, as the choices for 'incorrect segment' are usually more than those for correct segment, it is relatively hard for incorrect segments to converge to a few.

a correct segment should be more popular than any one single incorrect segment.


Given one selected segmentation S ij i , the objective is to sum up the consistencies between itself and any of the rest in matrix S Q INT .

by this objective, we choose the segmentations whose segments are agreed with by most top-n segmentations.


For example, to reject the transformation "diet plan → diet | plan", we have to use the token bigram <diet plan>.

we include all the token bigrams in the form of < w i 0 w i 0 +1 > as features as well.


Due to the ambiguity, a phrase in a query may be mistakenly linked to a certain entity in the knowledge base.

the semantic features include some noises.


LRMC relies on a training data which is automatically collected with the help of query intent sets.

in this section, we evaluate the training set collected by M1 and M2 (Section 4.2).


According to our analysis, most of the gap is caused by that the incorrect segmentations for some similar queries also happen to have a high consistency when measured by either proposed strategy.

it is worth studying other methods that can address such performance gap.


However, they focus on integrating term dependencies into their respective models without explicitly considering any syntactic or semantic structures in language.

the purpose of these studies can be considered different from ours.


They retrieved sentences that match a given query using predicate-argument structures via a framework of region algebra.

they namely approached the task of sentence matching, which is not the same as document retrieval (or ranking).


The corresponding function is ϕ BF (p, k), where p is a non-leaf node and k is the number of children to be selected for p. To avoid sparsity, we choose one of R + 1 features (1, • • • , R or >R) for some pre-defined R. To be precise, we fire two features per non-leaf node: one is node-specific and the other is shared among non-leaf nodes.

we append at most (I + 1)(R + 1) features to the global weight vector, where I is the number of non-leaf nodes.


However, also contrastive contextually bound expressions can follow the rhematizerstypically at the beginning of the sentence (and in this case, also the rhematizers are contextually bound).

only such rhematizers are pre-annotated as contextually non-bound that are not placed in the initial position in the sentence.


The final step of the automatic pre-annotation is based on the fact that the adnominal adjuncts modifying its governing noun (in the annotated corpus marked as RSTR) often have a very high degree of communicative dynamism because their primary function is to specify something.

they are pre-annotated as contextually bound (if they modify a non-bound element at the same time).


As the discussion suggests, animates should occur more frequently with the case markers towards the left of the Scale (1), while inanimates should occur more frequently with the ones towards the right of the Scale.

animates should have a leftskewed distribution on Scale (1), while inanimates should have a right-skewed distribution.


This interferes with evaluation, since we need to to be fairly certain not only which seeds are in a category, but which are not.

we carried out a full annotation study with 5 annotators, asking each annotator to tag all 900 words for each of the 6 styles according to guidelines we prepared.


The exact scope of the mention detection task varies considerably depending on the annotation guidelines.

some corpora consider all the (non-embedding) NPs to be mentions, some corpora do not allow for non-referential mentions and some do not mark singleton referential mentions, that do not participate in coreference relations.


Several algorithms have been proposed recently, trying to tackle some of the addressed phenomena within a computational approach.

a number of algorithms have been developed recently to identify expletive usages of "it" (Evans, 2001;Boyd et al., 2005;Bergsma and Yarowsky, 2011).


Social media and in particular Twitter has proven to be a faster channel of communication when compared to traditional news media, as we have witnessed during events such as the Middle East revolutions and the 2011 Japan earthquake; acting as social sensors of real-time events (Sakaki et al., 2010).

the identification of topics discussed in these channels could aid in different scenarios including violence detection and emergency response.


Since violence-related events tend to occur during short to medium life spans, traditional classification methods which rely on labelled data can rapidly become outdated.

in order to maintain tuned models it is necessary the continuous learning from social media in order to capture those features representing violent events.


For example the word "killing" may have a violent-related orientation as in "mass killing" while it has a non-violent one in "killing time".

detecting topic and violence-relatedness simultaneously should serve as a critical function in helping analysts by providing more informative violence-related topic mining results.


Each of these modules contains a set of linguistic rules.

each target word in the list obtains its variants.


As described in section 3.2, one Hangul syllable consists of several letters and, if the syllable is the target area of the reduction process, the contained vowel may be removed.

considering the first word of the Korean word, 한글 (hangul), Romanized as 'han', if one omits the vowel ('a') then the result would be 'hngul'.


Although the pipeline algorithm itself is a simple application of wellknown techniques (Xue, 2003;Peng et al., 2004;Neubig et al., 2011) and does not have much novelty, its effectiveness has been left unexplored in the context of lattice reranking.

its merits (or demerits) in relation to the pruningbased algorithm have also been unknown.


It also follows from the above discussion that the lattice size, that is, the number of edges, is also linear in the sentence length, i.e., O(|E|) = O(n).

since the node degree is at most α (i.e., not dependent on n), the lattice path can be efficiently reranked in O(n) time by using dynamic programming.


(2008) fixed pruning threshold K as 20, K ∈ {5, 10, 20} was tested to examine the effect of this parameter.

three rerankers that use the pruning-based algorithm were thus created.


Although Japanese is rich in onomatopoeias and some of them do not  appear in the lexicon, most of them follow several patterns such as 'ABAB,' 'A B ,' and 'AB ,' 1 and they thus can be resolved by considering typical patterns.

in this paper, we introduce derivation rules and onomatopoeia patterns to the unknown word processing in Japanese morphological analysis, and aim to resolve 1) unknown words derived from words in a pre-defined lexicon and 2) unknown onomatopoeias.


's approach on 2,000,000 blogs, and reported that their approach made 37.2% of the sentences affected by their method worse.

we conjecture that their approach only benefits a text that is very similar to the text in online chat conversations.


Compared to Table 1, we can see that the hybrid sequence model is still not good at handling long-distance ambiguities.

it harms the parsing performance (see Table 4), though it achieves higher overall precison.


In the listed example, first, second and the last four differ in terms of their origin and belong to different branches in the hierarchy.

they can not be grouped by optimal depth selection (described later in Section 6.2.3) and requires a manual scrutiny.


The improvement has been, as observed during error analysis, particularly for agents and patients.

semantics can be seen here as pseudo case markers.


In several NLP applications like information retrieval or machine translation it is important to identify LVCs in context since they require special treatment, particularly because of their semantic features.

lVCs should be identified to help these applications.


verb-object pairs -can be separated into two classes: one where the noun behaves as a real object (cake) and one where the noun functions as the light verb object (mistake).

lVC detection can be viewed as classifying certain syntactic patterns as lVCs or not and assigning a specific syntactic label to the argument of the light verb.


new (or emphatic) information (focus) always precedes the verb and old information (topic) precedes the focus position.

the position relative to the verb has no predictive force as regards the syntactic function of the given argument.


This treebank exists in another manually annotated version, namely, with dependency annotation .

manual annotations for LVCs and dependency structures are available for the same bunch of texts, which made it possible to map the two manual annotations.


However, evaluating agreement on such a formulation is not straightforward.

for the purpose of this IAA study, we port this task into a binary decision task of identifying whether participant X has power of type P or not.


7) "the large green bookcase near the chest" -like description 4, bookcase10 (which is green) is ranked second due to its low probability of being considered large.


Further, we insert the generic preposition "at" for a missing preposition.

we deviate from the noisy channel approach in that we do not quite reconstruct the original message.


• Objects and Landmarks -The expected PoS for Objects and Landmarks are: DT/JJ/VBG/VBD/NN for the first word, JJ/VBG/VBD/NN for the middle words, and NN for the last word.

if there is a PoS mismatch, we perform the following replacements (if there is only one word in an Object or Landmark, we replace it with "thing" (NN)): -HWord 1 ⇒"the" (DT) -HWord mid ⇒"unknown" (JJ) (multiple times) -HWord last ⇒"thing" (NN) To illustrate this process, consider the heard Object "to:TO battle:NN played:VB", which is replaced with "the:DT battle:NN thing:NN".


However, at present, we replace words that do not match an expected PoS only with generic options, e.g., "unknown" for expected adjectives, "thing" for expected nouns in Objects and Landmarks, and "position" for expected positional nouns in Specifiers.

our penalty consists only of the first of the above factors moderated by a generic similarity factor δ(= 0.5), as follows: Pr(replace HWord ji ) = (2) δ Pr(IsW(HWord ji ))Pr(Class) if label = W δ (1−Pr(IsC(HWord ji )))Pr(Class) if label = C In the future, the generic δ will be replaced by a function of the similarity between an original word and its candidate replacements.


Figure 1 also shows how combining multiple triangulated systems can reduce this number from 2600 (16%) OOVs to 1536 (9%) OOVs.

combining triangulated systems with the original src tgt system is a good idea.


These ensembles are separately tuned and once completed, these weights comprise the final tuned weights.

the total number of ensemble evaluations reduces from O(n 4 ) to O(3n).


Moreover, some of these sentences are not as informative and could be summarized to make a more cohesive document.

for tasks in which complete translation is not mandatory, MT can be effective if the system can provide a more informative subset of the content with higher translation quality.


However, having a noisy source language text for an MT system can degrade the translation quality dramatically.

extractive summarization like our framework is more suitable for MT summarization.


The automatic labeling procedure (section 4.1) enforces a rough 50-50 high and low quality translations.

we obtaine 1370 negative examples and 1363 positive ones.


One of pitfalls is that these features are so sparse that many features which are potentially useful for a test set may not be included in a given tuning set, and many useless features for testing will be over tuned on the developement set meanwhile.

the generalization abilities of features are limited due to the mismatch between the testing data and the tuning data, and over-fitting occurs.


These requirements can explain why tuning on the training data leads to unsatisfactory performance on the IWSLT translation task, as will be shown in our experiments later.

enlarging a tuning set is not always a sufficient solution for robust tuning, since it would be impractical to create a large scale tuning set with these requirements.


Our assumption is that although a feature which is useful for a test set does not appear in the tuning set, another similar feature may exist.

grouping similar features can alleviate sparsity in this way.


The above Lemma states that the optimal solution W t+1 in line 5 of Algorithm 2 is also a sparse vector.

it is desirable to optimize the W t+1 in a low complexity independent on d. If so, we can easily see that if we set W 1 as a sparse vector, W t is sparse for all t > 1 by a mathematical induction.


And v(g) can be intuitively interpreted as the group averaged sub-gradient of In addition, an intuitive explanation of merging loop is that the value of objective in Eq.4 will be decreased after each merging step in line 6.

in summary, if we use a sparse representation for vector a in Algorithm 3, then its complexity is O |∆(a)|log(|∆(a)|) , which is independent of d. the whole tuning algorithm (Algorithm 1) with feature grouping is efficient even with a large value of d. We conduct experiments on the iWSLT2008 Chinese-to-English translation tasks, whose training data consists of about 30K bilingual sentence 3 The reason is attributed to the Eq.5 in (Zhong and Kwok, 2011).


Set W as another weight such thatŴ j =Ŵ j for all j(j = k), andŴ k = 0.

then, for each i, j the following equations hold: and thus, the following equations hold based on the above equations by simple algebraic operations: we conclude that Q(Ŵ ; a, t, λ 1 , λ 2 ) > Q(Ŵ ; a, t, λ 1 , λ 2 ).


We use the extracted dataset by both methods as additional SMT training data, and measure the quality of the parallel data by its impact on the performance of the SMT system.

the final extracated parallel data is injected into the baseline system.


We keep only the sentences or phrases which have a TER score below a certain threshold determined empirically.

we filter the selected sentences or phrases in each condition with different TER thresholds ranging from 0 to 100 by steps of 10.


For example, all IMDB webpages describing video information match pattern "http://www.imdb.com/title/tt\d+/$".

we can take the url pattern as the identity of the website.


Likewise, if a pattern of class c i can also be generated by seeds from other categories, this pattern is obviously not a high-quality pattern for category c i .

we can score the patterns and seeds of the target category with the help of the other classes, which is termed "inter-class scoring".


Google and Twitter search are used to assist mannual annotations of events.

4249 event clusters in Gset were manually labeled into 804 news events and 3445 nonevents.


Their motivation is to discover novel events for a new domain rather than build a new event type paradigm from scratch.

it takes domain specific event triggers as the input.


1 The meaning of LVCs can only partially be computed on the basis of the meanings of their parts and the way they are related to each other (semicompositionality).

the result of translating their parts literally can hardly be considered as the proper translation of the original expression.


Its major difficulty arises from the lack of reliably labeled data, because it is extremely difficult for humans to identify through reading (see discussion in Section 4).

much previous work on detecting deceptive opinions usually relies on some meta-information, such as the IP address of the reviewer or the average rating of the product, rather than the actual content of the review Liu (2012).


On the other hand, since for truthful reviews, we are able to select a small subset for which we have high confidence only by using a combination of various heuristics, the constructed dataset is inherently biased.

given the limited number and size of currently available gold-standard datasets, in the process of developing more sophisticated models involving a larger number of features, we may inevitably face the issue of overfitting on a relatively small set of data.


obtained their best performance using unigrams alone, together with deep syntax features.

for a fair comparison, we consider using unigrams, bigrams, and the union of both, and choose the best combination with deep syntax features as our baseline system.


For each direction of the bidirectional alignment, we compute a list of aspect-wise compatibility features.

without loss of generality, assume that the direction of the alignment is Q → P. for each aspect a i in P, we include three features to indicate the compatibility between P and Q: 1. m a i : a boolean value indicating whether a i is mentioned in Q or not.


These formulae were not initially intended to be used for the evaluation of text simplification systems but rather to measure the grade level necessary to understand a given text.

we wanted to establish whether those readability indices could be used in an automatic evaluation of text simplification systems.


Furthermore, it seems that those three readability indices complement each other very well in terms of their correlation with different complexity features.

it might be possible to find some combination of all three of them which could be used as a single measure in an automatic evaluation of text simplification systems.


The ultimate source of the proposition expressed in the embedded sentence is not known since it is denoted by the pronoun some.

it is not known who provided the opinion and therefore it is uncertain whether this is an important (reliable) piece of information (e.g.


In this paper, we extend the original notion of weasel and we argue that propositions that have an underspecified argument that would be relevant or is not common knowledge in the situation can be also viewed as weasels.

a proposition is considered to be an instance of weasel if any of its relevant arguments is underspecified, i.e.


Figure 1 shows the relationship of hedge types relative to the reference point.

each type of hedge denotes a set in which the exact amount, quality or frequency of the relevant event or entity is situated but its exact place remains unclear.


People's opinions may differ from each other concerning specific things or events: they do not necessarily agree on what is good, neutral or bad.

we cannot unequivocally determine what is good or what is bad.


There is a great diversity among individual views and opinions: a feature of a product may be appreciated by some customers but it might be considered intolerable for others.

what should be considered positive or negative seems subjective.


These are responsible for about 86%, 45% and 42% of the occurrences of weasel, hedge and peacock cues, respectively.

a limited vocabu-  lary can account for over 85% of weasels.


6 Discourse-level Uncertainty and NLP Detecting weasels is of utmost importance in every information extraction application where it should be known who the author/source is.

information extraction applied for the news media may certainly profit from finding weasels, i.e.


For instance, if someone looks for websites describing games appropriate for ten year old children, he also may be interested in games that are for children over eight.

the search engine should be prepared for recognizing that the number specified in the query ("ten") is part of other numeric sets (e.g.


hedges are used) in order to prevent further abuse (Osenga, 2006).

the scope of the patents can be expanded or other use cases can later be included in the patent.


Moreover, two-level taxonomy allows us to map a tagset into different traditional schemes easily, such as CoNLL or MUC.

aCE (2008) taxonomy was selected and because it is designed for a news-wire domain we applied some amendments to tailor it for use in a relatively open domain corpus, such as Wikipedia.


Table 3: The average fine-grained classification results when using SGD and SVM over different features sets where (tf-idf) is applied Compilation of the final corpus was achieved according to the pipeline steps as follows: As a convention, a linking phrase in the text of any Wikipedia article should only be assigned the first time it appears in context; successive mentions of the phrase appear with no link.

not all NE phrases are linked every time.


For example ﻭﻟﺴﻌﻮد( /wlsςwd/ 'and for Saud').

we prepare for and match all the variations of prefixes that can be attached to the NE.


German term "Produktionsstandort" is translated into French by "site de production").

the distributional approach may consider such German terms as one word when computing co-occurrences.


To avoid redundancy, the speaker elides the scope of the second abilitive -ا ﻋﺮف AErf (can) -and does not replace it with any deictic expression.

aErf modifies an implicit verbal-phrase scope.


Modality-related information presents chunks of the tweet texts that represent the trigger word/phrase and the scope clause/verbal phrase.

we assume that we are not violating the terms of services.


For the other annotator, this trigger still means explain, but not as in explaining factual information, but as in making people adopt a specific point of view or a belief.

it is synonymous to ﯾﺆﻣﻨﻮ ﻧﺠﻌﻠﮭﻢ ن أن njElhm y&mnwn >n (make them believe that) and is an epistemic trigger.


Nowadays in tunisia, the arabic Tunisian Dialect (TD) has become progressively used in interviews, news and debate programs instead of Modern Standard Arabic (MSA).

this gave birth to a new kind of language.


Indeed, the majority of speech is no longer made in MSA but alternates between MSA and Tunisian Dialect (TD).

we can distinguish in the same speech, MSA words, TD words and MSA-TD words such as a word with an MSA component (root) and dialectal affixes.


As we aim to adapt MSA tools to TD, we tried to build for TD verbs the same concepts as those in MSA.

we focused in this work on the study of correspondences that may exist among the concepts of MSA verbs and dialect verbs.


1We have already assigned to ْ خ َج ر / xoraj the -TUN-pattern: CoCaC.

ْ س ت ُ ك / sokut will have the pattern -TUN: CoCuC (2).


Still, the performance of the merged dictionary did not improve dramatically.

we contrived a way to update the merged dictionary's entries to enhance performance.


With regard to the assumption (2) above, we found that contextual adjustment of sentiment scores was necessary for the given domain.

we proposed the 'switch' procedure which switched the polarity of selected dictionary entries.


By applying various rooting schemes at different granularities in two separate domains, it is also shown that word roots can serve to enhance the sentiment analysis task results on a more generic level, instead of using a domain-specific approach that may not always be applicable.

using root derivation techniques such as that presented for Arabic sentiment analysis in particular are applicable and valid to help enhance the performance of natural language processing tasks on morphologically rich and complex languages.


Here we describe the results of the best-performing combination of classifier and measure: a Decision Tree classifier ('J48') with one single feature (standard-cosine, or cosine-difference values).

for each of the experimental settings described above we run the classifier twice.


Since WIKIMUSENSE is still using machine translation when interlingual links are missing, we ran an additional experiment in which MT was completely removed during training to demonstrate the advantage of sense-annotated corpora available in supporting language Wikipedias.

for the 105 ambiguous words, we eliminated all senses that required machine translation to fill the sense gaps.


Unfortunately, in retrieval, we often have to estimate a model based on a small sample of data (e.g., a single document or only a few documents).

given limited data sampling, a language model estimation sometimes encounters with the zero count problem: the maximum likelihood estimator would give unseen terms a zero probability, which is not reliable because a larger sample of the data would likely contain the term.


Although low mutual information is proved to be less dependent, high mutual information does not necessarily guarantee high association, especially for low-frequency terms.

we apply the Refined Mutual Information (RMI) as an improvement (Manning and Schütze, 1999).


Since we have two smoothed language distributions, i.e., D s (i|d) and D p (i|d), with uniform representation, we can combine both smoothing  w) is to measure the semantic association between w and the word w i from position i, and ψ(i, j) is the distance discount factor.

the language model of this virtual document can be estimated as: where V is the vocabulary set. )


The effect is even more dramatic in our model, as each word in a sentence has a ⟨/L⟩ and a ⟨/R⟩ tag to normalize its modifier numbers, so the token number in a sentence is multiplied by.

we only count the lexical words in perplexity calculation for fairness.


7 run-time in ms per sentence 0 0.4 0.8 1.2 1.6 0 0.4 0.8 1.2 1.6 (∏ pre , ∏ 2 ) 0.65  The online adaptation algorithm did not succeed on D 0 , since its mean run-time prediction error of 0.45ms was almost half as high as the average run-times to be predicted, which is too inaccurate under the small differences in the pipelines' runtimes.

for only 39% of the input texts, the best pipeline was chosen (i.e., a classification error of 0.61).


However, the impact on D 3 does not emanate from a low mean prediction error (that was in fact 0.24ms higher), but the classification error was reduced to 0.45.

the main reason lies in larger differences in the pipelines' run-times, which supports our hypothesis.


Although scientific articles usually provide them, most of the documents have no associated keyphrases.

the problem of automatically assigning keyphrases to documents is an active field of research.


Keyphrase candidates are usually treated independently and the information about the topic they represent is scattered throughout the graph.

we propose to group similar noun phrases as a single entity, a topic.


t i and t j have a strong semantic relation if their keyphrase candidates often appear close to each other in the document.

the weight w i,j of their edge is defined as follows: where dist(c i , c j ) refers to the reciprocal distances between the offset positions of the candidate keyphrases c i and c j in the document and where pos(c i ) represents all the offset positions of the candidate keyphrase c i .


Based on analysis of NL queries from that domain, we observe that most queries carry an underlying structure.

a set of CFG rules were written for the map search scenario.


Most of researchers investigate English reviews rather than ones with other languages.

we motivate to explore the movie reviews on Chinese Bulletin Board System (BBS).


It is due to no syntactic information is adapted.

we introduce a Chinese parser and try to solve this problem.


Consider the case where the functional relation "a camera is usedfor tak-ing_picture" may be of more interest to an individual than the hierarchical relation "a camera hasa tripod".

a product which takes good pictures but lacks a tripod will have a high positive polarity.


In case the parent feature polarity is neutral, its polarity is given by its children feature polarities.

the information at a particular node is given by its self information and the weighted information of all its children nodes.


Furthermore, in calculating the harmfulness score we apply dependency relations between phrases.

there is no need to check all words proceeding and succeeding the queried words, which greatly reduces processing time.


Countless number of various pages exists on the Web, and thus various words are written there.

it is possible to obtain a high coverage by using the IR score.


From the above results, we concluded that about 12% of all entries appearing on informal school BBS can be accounted as cyberbullying.

in the experiment we verified the performance of the method under the condition when harmful entries cover 12% of the whole data.


In the future for automatic threshold setting we plan to develop a machine learning method capable of handling small sized data.

this time we increased the threshold manually each time and investigated Precision and Recall for each threshold.


This happened because for some harmful entries the harmfulness score could not be calculated highly enough due to the fact that the score calculation is more strict (Precision-oriented) in the proposed method.

although Recall is slightly higher in the baseline for large thresholds, the Recall is higher for the proposed method for small and medium thresholds, with the Precision being constantly higher for the proposed method.


For instance, in the case of the expression know that, it presupposes that the event in that-clause is fac-  tual.

the predicate know is a factuality marker which changes the factuality of the event in that-clause into CT+.


Our intent for this experiment is not to analyze this kind of error.

we use the gold label as Propagated Factuality in order to prevent the error propagation.